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  • Open Access
    Authors: 
    Liu, Mingming; Yang, Jing; Liu, Yushi; Jia, Bochao; Chen, Yun-Fei; Sun, Luna; Ma, Shujie;
    Publisher: Taylor & Francis
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis.

  • Authors: 
    Giorgio, Joseph; Landau, Susan; Jagust, William; Tino, Peter; Kourtzi, Zoe;
    Country: United Kingdom
    Project: CIHR , UKRI | Flexible perception: func... (BB/P021255/1), WT , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Multimodal biological and cognitive data used as predictors and outcomes for machine learning models can be found in 'master data sheet.xls'. With the exception of the derived PLS Derived GM all data were downloaded from the ADNI repository http://adni.loni.usc.edu/. For description on derivation of the PLS Dervived GM see ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� in the final publication DATA SETS: 1.) ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2MCI): Baseline diagnosis (1:cognitively normal, 2: MCI) ADNI Mem: ADNI Memory composite measure used as outcome variable for the PLSr-RFE, PLS Derived GM: Variable derived from the PLSr-RFE procedure. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 2.) ���Statistical Validation: Out-of-Sample validation[cross-modality associations ]��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2DEM, 3MCI): Baseline diagnosis (1:cognitively normal, 2:demented, 3: MCI), PLS Derived GM: Variable derived out-of-sample. FTP Braak(12): tau PET SUVR for Braak stage (1,2), FTP Braak(34): tau PET SUVR for Braak stage (3,4), FTP Braak(56): tau PET SUVR for Braak stage (5,6). These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 3.)���Statistical Validation: Out-of-Sample validation [Cross-modal associations -adni mem]��� Data available: RIDS: The ADNI identifier ADNI Mem: ADNI Memory composite measure used as outcome variable. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 4.) ��� Methods:GMLVQ Cognitive model��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Cognitive Classification Models for predicting sMCI vs pMCI��� 5.) ��� Methods:GMLVQ Biological model��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Biological Classification Models for predicting sMCI vs pMCI��� 6.) ��� Methods: GMLVQ-Scalar Projection *Cognitive model*��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 7.) ��� Methods: GMLVQ-Scalar Projection *Biological model*��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline. 8.) ���Methods: Statistical Validation: Out-of-Sample-[Cognitive model]��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 9.) ���Methods: Statistical Validation: Out-of-Sample-[Biological model]��� : RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline.��� For a more detailed description of the populations these data were extracted for see 'description of uploaded files.doc'

  • Open Access
    Authors: 
    Woo, Young; Roussos, Panos; Haroutunian, Vahram; Katsel, Pavel; Gandy, Samuel; Schadt, Eric; Zhu, Jun;
    Publisher: figshare
    Project: CIHR , NIH | Developing methods for cu... (5U01HG008451-02), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Accelerating Medicine Par... (3U01AG046170-01S1)

    Additional file 2: Table S1. Probability of tract reaching brain region (Reach probability). Table S2. Brain region mapping between Desikan-Killany atlas and post-mortem brain labels. Table S3. Number of Tissue-to-Tissue (TTC) correlated genes for each pair of Region-of-interests (ROIs). Table S4. Pathway list with annotated subtypes. Table S5. Pathway interactions in brain region pairs that are that are significantly different in tract-bound. Table S6. Pathway interactions in brain region pairs that are that are significantly different in AD-tract-bound. Table S7. Association between covariates and diffusion measures in each tract. Table S8. Effect sizes for associations in ADNI3 and ADNI2. Table S9. Pathway over-representation analysis between brain region pairs connected by white matter tracts and region pairs not connected by tracts. Table S10. Pathway interaction graph (degree). Table S11. Pathway over-representation analysis of symmetric gene synchronization in brain region pairs connected by white matter tracts. Table S12. Association between gene expression of Toll receptor signaling in the blood and diffusion measures in the brain. Table S13. Number of subjects in each study per site.

  • Open Access
    Authors: 
    Chang, Yu-Chuan; June-Tai Wu; Hong, Ming-Yi; Yi-An Tung; Ping-Han Hsieh; Yee, Sook; Giacomini, Kathleen; Yen-Jen Oyang; Chien-Yu Chen;
    Publisher: figshare
    Project: NIH | CORE-- EDUCATION AND INFO... (5P30AG010133-08), CIHR , NSF | III: Small: Collaborative... (1117335), NIH | Memory Circuitry in MCI a... (2R01AG019771-06), NIH | Metabolic Networks and Pa... (5R01AG046171-03), NIH | Bioinformatics Strategies... (5R01LM011360-03), NIH | CORE-- CLINICAL (3P30AG010129-11S1), NIH | Amyloid Imaging, VMCI, an... (1RC2AG036535-01), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Indiana Clinical and Tran... (3UL1TR001108-02S1),...

    Additional file 2. The R script to draw a heatmap for GTEx dataset.

  • Open Access
    Authors: 
    John-Williams, Lisa St.; Siamak Mahmoudiandehkordi; Arnold, Matthias; Massaro, Tyler; Blach, Colette; Kastenmüller, Gabi; Louie, Gregory; Kueider-Paisley, Alexandra; Xianlin Han; Baillie, Rebecca; +12 more
    Publisher: figshare
    Project: NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR , NIH | Metabolic Networks and Pa... (5R01AG046171-03), NIH | Modulation of regulatory ... (5R01CA151550-02)

    This dataset contains key characteristics about the data described in the Data Descriptor Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

  • Open Access
    Authors: 
    Konijnenberg, Elles; Tijms, Betty M.; Gobom, Johan; Dobricic, Valerija; Bos, Isabelle; Vos, Stephanie; Tsolaki, Magda; Verhey, Frans; Popp, Julius; Martinez-Lage, Pablo; +13 more
    Publisher: figshare
    Project: CIHR , EC | EMIF (115372), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Additional file 1. Supplementary tables.

  • English
    Authors: 
    Ledig, Christian; Schuh, Andreas; Guerrero, Ricardo; Heckemann, Rolf A.; Rueckert, Daniel;
    Publisher: G-Node
    Project: EC | PREDICTND (611005), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR , UKRI | Dementia Diagnosis: A too... (101685)

    Data accompanying the article: C. Ledig, A. Schuh, R. Guerrero, R. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, 2018. Data derived from 5074 images from the ADNI cohort: - structural segmentations (138 regions, MALPEM); - binary brain masks (pincram); - features (volumes, asymmetry, atrophy rates) and disease labels; - lists of processed images IsSupplementTo: Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports, 2018. https://doi.org/10.1038/s41598-018-29295-9

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    The correlation between gender and CSF BACE activity. (TIF 2480 kb)

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    The MDS plot of samples. (TIF 8989 kb)

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR

    The correlation between age and CSF BACE activity. (TIF 2436 kb)

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
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arrow_drop_down
is
arrow_drop_down
Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
21 Research products, page 1 of 3
  • Open Access
    Authors: 
    Liu, Mingming; Yang, Jing; Liu, Yushi; Jia, Bochao; Chen, Yun-Fei; Sun, Luna; Ma, Shujie;
    Publisher: Taylor & Francis
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis.

  • Authors: 
    Giorgio, Joseph; Landau, Susan; Jagust, William; Tino, Peter; Kourtzi, Zoe;
    Country: United Kingdom
    Project: CIHR , UKRI | Flexible perception: func... (BB/P021255/1), WT , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Multimodal biological and cognitive data used as predictors and outcomes for machine learning models can be found in 'master data sheet.xls'. With the exception of the derived PLS Derived GM all data were downloaded from the ADNI repository http://adni.loni.usc.edu/. For description on derivation of the PLS Dervived GM see ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� in the final publication DATA SETS: 1.) ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2MCI): Baseline diagnosis (1:cognitively normal, 2: MCI) ADNI Mem: ADNI Memory composite measure used as outcome variable for the PLSr-RFE, PLS Derived GM: Variable derived from the PLSr-RFE procedure. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 2.) ���Statistical Validation: Out-of-Sample validation[cross-modality associations ]��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2DEM, 3MCI): Baseline diagnosis (1:cognitively normal, 2:demented, 3: MCI), PLS Derived GM: Variable derived out-of-sample. FTP Braak(12): tau PET SUVR for Braak stage (1,2), FTP Braak(34): tau PET SUVR for Braak stage (3,4), FTP Braak(56): tau PET SUVR for Braak stage (5,6). These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 3.)���Statistical Validation: Out-of-Sample validation [Cross-modal associations -adni mem]��� Data available: RIDS: The ADNI identifier ADNI Mem: ADNI Memory composite measure used as outcome variable. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 4.) ��� Methods:GMLVQ Cognitive model��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Cognitive Classification Models for predicting sMCI vs pMCI��� 5.) ��� Methods:GMLVQ Biological model��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Biological Classification Models for predicting sMCI vs pMCI��� 6.) ��� Methods: GMLVQ-Scalar Projection *Cognitive model*��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 7.) ��� Methods: GMLVQ-Scalar Projection *Biological model*��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline. 8.) ���Methods: Statistical Validation: Out-of-Sample-[Cognitive model]��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 9.) ���Methods: Statistical Validation: Out-of-Sample-[Biological model]��� : RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline.��� For a more detailed description of the populations these data were extracted for see 'description of uploaded files.doc'

  • Open Access
    Authors: 
    Woo, Young; Roussos, Panos; Haroutunian, Vahram; Katsel, Pavel; Gandy, Samuel; Schadt, Eric; Zhu, Jun;
    Publisher: figshare
    Project: CIHR , NIH | Developing methods for cu... (5U01HG008451-02), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Accelerating Medicine Par... (3U01AG046170-01S1)

    Additional file 2: Table S1. Probability of tract reaching brain region (Reach probability). Table S2. Brain region mapping between Desikan-Killany atlas and post-mortem brain labels. Table S3. Number of Tissue-to-Tissue (TTC) correlated genes for each pair of Region-of-interests (ROIs). Table S4. Pathway list with annotated subtypes. Table S5. Pathway interactions in brain region pairs that are that are significantly different in tract-bound. Table S6. Pathway interactions in brain region pairs that are that are significantly different in AD-tract-bound. Table S7. Association between covariates and diffusion measures in each tract. Table S8. Effect sizes for associations in ADNI3 and ADNI2. Table S9. Pathway over-representation analysis between brain region pairs connected by white matter tracts and region pairs not connected by tracts. Table S10. Pathway interaction graph (degree). Table S11. Pathway over-representation analysis of symmetric gene synchronization in brain region pairs connected by white matter tracts. Table S12. Association between gene expression of Toll receptor signaling in the blood and diffusion measures in the brain. Table S13. Number of subjects in each study per site.

  • Open Access
    Authors: 
    Chang, Yu-Chuan; June-Tai Wu; Hong, Ming-Yi; Yi-An Tung; Ping-Han Hsieh; Yee, Sook; Giacomini, Kathleen; Yen-Jen Oyang; Chien-Yu Chen;
    Publisher: figshare
    Project: NIH | CORE-- EDUCATION AND INFO... (5P30AG010133-08), CIHR , NSF | III: Small: Collaborative... (1117335), NIH | Memory Circuitry in MCI a... (2R01AG019771-06), NIH | Metabolic Networks and Pa... (5R01AG046171-03), NIH | Bioinformatics Strategies... (5R01LM011360-03), NIH | CORE-- CLINICAL (3P30AG010129-11S1), NIH | Amyloid Imaging, VMCI, an... (1RC2AG036535-01), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Indiana Clinical and Tran... (3UL1TR001108-02S1),...

    Additional file 2. The R script to draw a heatmap for GTEx dataset.

  • Open Access
    Authors: 
    John-Williams, Lisa St.; Siamak Mahmoudiandehkordi; Arnold, Matthias; Massaro, Tyler; Blach, Colette; Kastenmüller, Gabi; Louie, Gregory; Kueider-Paisley, Alexandra; Xianlin Han; Baillie, Rebecca; +12 more
    Publisher: figshare
    Project: NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR , NIH | Metabolic Networks and Pa... (5R01AG046171-03), NIH | Modulation of regulatory ... (5R01CA151550-02)

    This dataset contains key characteristics about the data described in the Data Descriptor Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

  • Open Access
    Authors: 
    Konijnenberg, Elles; Tijms, Betty M.; Gobom, Johan; Dobricic, Valerija; Bos, Isabelle; Vos, Stephanie; Tsolaki, Magda; Verhey, Frans; Popp, Julius; Martinez-Lage, Pablo; +13 more
    Publisher: figshare
    Project: CIHR , EC | EMIF (115372), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    Additional file 1. Supplementary tables.

  • English
    Authors: 
    Ledig, Christian; Schuh, Andreas; Guerrero, Ricardo; Heckemann, Rolf A.; Rueckert, Daniel;
    Publisher: G-Node
    Project: EC | PREDICTND (611005), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR , UKRI | Dementia Diagnosis: A too... (101685)

    Data accompanying the article: C. Ledig, A. Schuh, R. Guerrero, R. Heckemann, D. Rueckert, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, Scientific Reports, 2018. Data derived from 5074 images from the ADNI cohort: - structural segmentations (138 regions, MALPEM); - binary brain masks (pincram); - features (volumes, asymmetry, atrophy rates) and disease labels; - lists of processed images IsSupplementTo: Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Reports, 2018. https://doi.org/10.1038/s41598-018-29295-9

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    The correlation between gender and CSF BACE activity. (TIF 2480 kb)

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: CIHR , NIH | Alzheimers Disease Neuroi... (1U01AG024904-01)

    The MDS plot of samples. (TIF 8989 kb)

  • Open Access
    Authors: 
    Hu, Hao; Haiyan Li; Jieqiong Li; Jintai Yu; Tan, Lan;
    Publisher: figshare
    Project: NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), CIHR

    The correlation between age and CSF BACE activity. (TIF 2436 kb)